{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec592e13",
   "metadata": {},
   "source": [
    "## 优衣库简介\n",
    "优衣库（英文名称：UNIQLO，日文假名发音：ユニクロ），为日本迅销公司的核心品牌,建立于1984年，当年是一家销售西服的小服装店，现已成为国际知名服装品牌。优衣库现任董事长兼总经理柳井正在日本首次引进了大卖场式的服装销售方式，通过独特的商品策划、开发和销售体系来实现店铺运作的低成本化，由此引发了优衣库的热卖潮。\n",
    "\n",
    "优衣库(Uniqlo) 的内在涵义是指通过摒弃了不必要装潢装饰的仓储型店铺，采用超市型的自助购物方式，以合理可信的价格提供顾客希望的商品价廉物美的休闲装“UNIQLO”是Unique Clothing Warehouse的缩写，意为消费者提供“低价良品、品质保证”的经营理念，在日本经济低迷时期取得了惊人的业绩。\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942019bd",
   "metadata": {},
   "source": [
    "## 目标\n",
    "以下将根据优衣库的销售数据，利用Python进行可视化，并尝试回答如下问题：\n",
    "\n",
    "#### - 整体销售情况随着时间的变化是怎样的？\n",
    "#### - 不同产品的销售情况是怎样的？顾客偏爱哪一种购买方式？\n",
    "#### - 销售额和产品成本之间的关系怎么样？\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97fde78a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:17.220153600Z",
     "start_time": "2024-08-11T06:24:17.194307300Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41c02269",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:17.966545500Z",
     "start_time": "2024-08-11T06:24:17.952947500Z"
    }
   },
   "outputs": [],
   "source": [
    "# 解决matplotlib中，不能显示汉字的问题\n",
    "# Windows 下\n",
    "plt.rcParams['font.family'] = 'SimHei'  # 设置字体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 正常显示字符\n",
    "# Mac OS  下\n",
    "# plt.rcParams['font.family'] = 'Heiti TC'  # 设置字体\n",
    "# plt.rcParams['axes.unicode_minus'] = False  # 正常显示字符\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5278c9f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:18.780653600Z",
     "start_time": "2024-08-11T06:24:18.682128500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       store_id city channel gender_group age_group  wkd_ind product  \\\n0           658   深圳      线下       Female     25-29  Weekday    当季新品   \n1           146   杭州      线下       Female     25-29  Weekday      运动   \n2            70   深圳      线下         Male      >=60  Weekday      T恤   \n3           658   深圳      线下       Female     25-29  Weekday      T恤   \n4           229   深圳      线下         Male     20-24  Weekend      袜子   \n...         ...  ...     ...          ...       ...      ...     ...   \n22288       146   杭州      线下       Female     30-34  Weekday      短裤   \n22289       430   成都      线下       Female     25-29  Weekend      T恤   \n22290       449   武汉      线下       Female     35-39  Weekday      T恤   \n22291       758   杭州      线下       Female     20-24  Weekday      袜子   \n22292       616   成都      线下         Male     30-34  Weekday    当季新品   \n\n       customer  revenue  order  quant  unit_cost  unit_price  \n0             4    796.0      4      4         59         199  \n1             1    149.0      1      1         49         149  \n2             2    178.0      2      2         49          89  \n3             1     59.0      1      1         49          59  \n4             2     65.0      2      3          9          22  \n...         ...      ...    ...    ...        ...         ...  \n22288         1     80.0      1      2         19          40  \n22289         1     79.0      1      1         49          79  \n22290         1    158.0      1      2         49          79  \n22291         1     26.0      1      1          9          26  \n22292         1     79.0      1      1         59          79  \n\n[22293 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_id</th>\n      <th>city</th>\n      <th>channel</th>\n      <th>gender_group</th>\n      <th>age_group</th>\n      <th>wkd_ind</th>\n      <th>product</th>\n      <th>customer</th>\n      <th>revenue</th>\n      <th>order</th>\n      <th>quant</th>\n      <th>unit_cost</th>\n      <th>unit_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>658</td>\n      <td>深圳</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekday</td>\n      <td>当季新品</td>\n      <td>4</td>\n      <td>796.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>59</td>\n      <td>199</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>146</td>\n      <td>杭州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekday</td>\n      <td>运动</td>\n      <td>1</td>\n      <td>149.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>149</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>70</td>\n      <td>深圳</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>&gt;=60</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>2</td>\n      <td>178.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>49</td>\n      <td>89</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>658</td>\n      <td>深圳</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>59.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>229</td>\n      <td>深圳</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>20-24</td>\n      <td>Weekend</td>\n      <td>袜子</td>\n      <td>2</td>\n      <td>65.0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>9</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>22288</th>\n      <td>146</td>\n      <td>杭州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>30-34</td>\n      <td>Weekday</td>\n      <td>短裤</td>\n      <td>1</td>\n      <td>80.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>19</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>22289</th>\n      <td>430</td>\n      <td>成都</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekend</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>79.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>22290</th>\n      <td>449</td>\n      <td>武汉</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>35-39</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>158.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>49</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>22291</th>\n      <td>758</td>\n      <td>杭州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>20-24</td>\n      <td>Weekday</td>\n      <td>袜子</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>22292</th>\n      <td>616</td>\n      <td>成都</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>30-34</td>\n      <td>Weekday</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>79.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>79</td>\n    </tr>\n  </tbody>\n</table>\n<p>22293 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniqlo_data=pd.read_csv('data/uniqlo.csv',encoding='utf-8')\n",
    "uniqlo_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27538957",
   "metadata": {},
   "source": [
    "### 数据集字段说明\n",
    "\n",
    "| 字段名          | 说明       |\n",
    "| --------------- | ---------- |\n",
    "| **store_id** | 门店随机编号id,无实际意义 |\n",
    "| **city** | 门店所在城市 |\n",
    "| **channel** | 门店所产生的销售渠道，线上表示网上购买到门店自提，线下表示门店直接购买 |\n",
    "| **gender_group** | 客户性别男/女 |\n",
    "| **age_group** | 客户年龄段 |\n",
    "| **wkd_ind** | 购买发生的时间(周末，周中) |\n",
    "| **product** | 产品类别 |\n",
    "| **customer** | 客户数量 |\n",
    "| **revenue** | 销售金额 |\n",
    "| **order** | 订单数量(一 个客人可能多次购买) |\n",
    "| **Quant** | 购买的产品数量 |\n",
    "| **unit_cost** | 产品的成本(包含制造和营销层面) |\n",
    "| **unit_price** | 产品的单价(由销售金额/产品数量获得) |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22ceb887",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:20.378514Z",
     "start_time": "2024-08-11T06:24:20.347258400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22293 entries, 0 to 22292\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   store_id      22293 non-null  int64  \n",
      " 1   city          22293 non-null  object \n",
      " 2   channel       22293 non-null  object \n",
      " 3   gender_group  22293 non-null  object \n",
      " 4   age_group     22293 non-null  object \n",
      " 5   wkd_ind       22293 non-null  object \n",
      " 6   product       22293 non-null  object \n",
      " 7   customer      22293 non-null  int64  \n",
      " 8   revenue       22293 non-null  float64\n",
      " 9   order         22293 non-null  int64  \n",
      " 10  quant         22293 non-null  int64  \n",
      " 11  unit_cost     22293 non-null  int64  \n",
      " 12  unit_price    22293 non-null  int64  \n",
      "dtypes: float64(1), int64(6), object(6)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据完整性\n",
    "uniqlo_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2268373",
   "metadata": {},
   "source": [
    "### 数据共计2万2千多条。没有空值等数据。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "           store_id      customer       revenue         order         quant  \\\ncount  22293.000000  22293.000000  22293.000000  22293.000000  22293.000000   \nmean     335.391558      1.629480    159.531371      1.651998      1.858072   \nstd      230.236167      1.785605    276.254066      1.861480      2.347301   \nmin       19.000000      1.000000     -0.660000      1.000000      1.000000   \n25%      142.000000      1.000000     64.000000      1.000000      1.000000   \n50%      315.000000      1.000000     99.000000      1.000000      1.000000   \n75%      480.000000      2.000000    175.000000      2.000000      2.000000   \nmax      831.000000     58.000000  12538.000000     65.000000     84.000000   \n\n          unit_cost    unit_price  \ncount  22293.000000  22293.000000  \nmean      46.124658     84.279998  \nstd       19.124347     46.314296  \nmin        9.000000      0.000000  \n25%       49.000000     56.000000  \n50%       49.000000     79.000000  \n75%       49.000000     99.000000  \nmax       99.000000    299.000000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_id</th>\n      <th>customer</th>\n      <th>revenue</th>\n      <th>order</th>\n      <th>quant</th>\n      <th>unit_cost</th>\n      <th>unit_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>335.391558</td>\n      <td>1.629480</td>\n      <td>159.531371</td>\n      <td>1.651998</td>\n      <td>1.858072</td>\n      <td>46.124658</td>\n      <td>84.279998</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>230.236167</td>\n      <td>1.785605</td>\n      <td>276.254066</td>\n      <td>1.861480</td>\n      <td>2.347301</td>\n      <td>19.124347</td>\n      <td>46.314296</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>19.000000</td>\n      <td>1.000000</td>\n      <td>-0.660000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>142.000000</td>\n      <td>1.000000</td>\n      <td>64.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>49.000000</td>\n      <td>56.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>315.000000</td>\n      <td>1.000000</td>\n      <td>99.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>49.000000</td>\n      <td>79.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>480.000000</td>\n      <td>2.000000</td>\n      <td>175.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>49.000000</td>\n      <td>99.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>831.000000</td>\n      <td>58.000000</td>\n      <td>12538.000000</td>\n      <td>65.000000</td>\n      <td>84.000000</td>\n      <td>99.000000</td>\n      <td>299.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据的统计描述\n",
    "uniqlo_data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:21.690701300Z",
     "start_time": "2024-08-11T06:24:21.670687200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 统计描述分析\n",
    "1. 每次下单平均 1.6 个人, 通过四分位数，大部分购买者，都是1~2人进店购买\n",
    "2. 平均每单消费 159 元， 一半的订单集中在 64元~175元之间\n",
    "3. 每次人均下单1.8单,\n",
    "4. 优衣库，最低商品的成本是 9元，最高成本99元，大部分在49元， 平均成本 19 元\n",
    "5. 通过 revenue 统计描述分析，max 部分过大，可能存在企业购买或个人批量订购"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c14d1ff1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:23.000905900Z",
     "start_time": "2024-08-11T06:24:22.990831900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "product\nT恤      10610\n当季新品     2540\n袜子       2053\n短裤       1694\n配件       1572\n牛仔裤      1412\n运动        976\n毛衣        807\n裙子        629\nName: count, dtype: int64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 了解离散型数据的情况\n",
    "# 1. 分析store_id 了解门店的数量\n",
    "uniqlo_data.store_id.value_counts().sort_values(ascending=False)\n",
    "#  结论: 门店数量共计 64 家, 编号为207 的门店订单数最多为 603单，\n",
    "#                          编号为 831的门店单数最少 179单\n",
    "\n",
    "# 2. 分析 city 了解门店的分布城市\n",
    "city_data = uniqlo_data.city.value_counts()\n",
    "#  结论: 门店分布在 10 家 \n",
    "#     分别是 ： 深圳，杭州，武汉，上海，广州，重庆，西安，成都，北京，南京\n",
    "#     其中南京的数据数量比较少,为 500单 ,深圳最多为 4364 单\n",
    "# city_data.plot.bar(rot=0)\n",
    "# city_data\n",
    "\n",
    "\n",
    "# 3. 分析 channel 了解渠道类型\n",
    "# uniqlo_data.channel.value_counts()\n",
    "#  结论: 渠道共分为两种类型： 线上和线下， 线下是主力，线上可能有潜力\n",
    "\n",
    "\n",
    "# 4. 分析 gender_group 了解 购买人群 性别 类型\n",
    "gender_data = uniqlo_data.gender_group.value_counts()\n",
    "#  结论: 性别共分为两种类型： 男 (Male) 和 女(Female)\n",
    "# gender_data.plot.pie(autopct='%.2f%%', fontsize=13)\n",
    "gender_data\n",
    "\n",
    "# 5. 分析 age_group 了解 购买人群 类型\n",
    "# uniqlo_data.age_group.value_counts()\n",
    "#  结论: 年龄共分为11种类型： 分别为 <20,20~24, ... 55-59， >=60，Unkown等11类\n",
    "#       中间每个类别间隔5岁\n",
    "#       购买的主力人群是 20~40 之间的人\n",
    "\n",
    "# 6. 分析 wkd_ind 了解 购买 时间 类型 \n",
    "uniqlo_data.wkd_ind.value_counts()\n",
    "# uniqlo_data.wkd_ind.value_counts().plot.bar()\n",
    "#  结论: 购买时间共分为两种类型： 周中 (Weekday) 和 周末(Weekend)\n",
    "#        周中 销量比较大，周末单日总量比较大，可以加派人手。主力销售在周中。\n",
    "\n",
    "# 7. 分析 product 了解 产品 类型 \n",
    "uniqlo_data['product'].value_counts()\n",
    "#  结论: 产品共分为9大类型： T恤、当季新品、袜子、短裤、配件、牛仔裤、运动、毛衣、裙子  \n",
    "#      T 恤 销售最好，是销售的主力"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "531201b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:23.910445900Z",
     "start_time": "2024-08-11T06:24:23.891469700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "           store_id      customer       revenue         order         quant  \\\ncount  22293.000000  22293.000000  22293.000000  22293.000000  22293.000000   \nmean     335.391558      1.629480    159.531371      1.651998      1.858072   \nstd      230.236167      1.785605    276.254066      1.861480      2.347301   \nmin       19.000000      1.000000     -0.660000      1.000000      1.000000   \n25%      142.000000      1.000000     64.000000      1.000000      1.000000   \n50%      315.000000      1.000000     99.000000      1.000000      1.000000   \n75%      480.000000      2.000000    175.000000      2.000000      2.000000   \nmax      831.000000     58.000000  12538.000000     65.000000     84.000000   \n\n          unit_cost    unit_price  \ncount  22293.000000  22293.000000  \nmean      46.124658     84.279998  \nstd       19.124347     46.314296  \nmin        9.000000      0.000000  \n25%       49.000000     56.000000  \n50%       49.000000     79.000000  \n75%       49.000000     99.000000  \nmax       99.000000    299.000000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_id</th>\n      <th>customer</th>\n      <th>revenue</th>\n      <th>order</th>\n      <th>quant</th>\n      <th>unit_cost</th>\n      <th>unit_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n      <td>22293.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>335.391558</td>\n      <td>1.629480</td>\n      <td>159.531371</td>\n      <td>1.651998</td>\n      <td>1.858072</td>\n      <td>46.124658</td>\n      <td>84.279998</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>230.236167</td>\n      <td>1.785605</td>\n      <td>276.254066</td>\n      <td>1.861480</td>\n      <td>2.347301</td>\n      <td>19.124347</td>\n      <td>46.314296</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>19.000000</td>\n      <td>1.000000</td>\n      <td>-0.660000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>142.000000</td>\n      <td>1.000000</td>\n      <td>64.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>49.000000</td>\n      <td>56.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>315.000000</td>\n      <td>1.000000</td>\n      <td>99.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>49.000000</td>\n      <td>79.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>480.000000</td>\n      <td>2.000000</td>\n      <td>175.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>49.000000</td>\n      <td>99.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>831.000000</td>\n      <td>58.000000</td>\n      <td>12538.000000</td>\n      <td>65.000000</td>\n      <td>84.000000</td>\n      <td>99.000000</td>\n      <td>299.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分析数值型数据的基本描述\n",
    "uniqlo_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b850f1",
   "metadata": {},
   "source": [
    "### 统计描述分析\n",
    "1. 每次下单平均 1.6 个人, 通过四分位数，大部分购买者，都是1~2人进店购买\n",
    "2. 平均每单消费 159 元， 一半的订单集中在 64元~175元之间\n",
    "3. 每次人均下单1.8单,\n",
    "4. 优衣库，最低商品的成本是 9元，最高成本99元，大部分在49元， 平均成本 19 元 \n",
    "5. 通过 revenue 统计描述分析，max 部分过大，可能存在企业购买或个人批量订购"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07ffb66b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T06:24:59.113188100Z",
     "start_time": "2024-08-11T06:24:59.081589400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       store_id city channel gender_group age_group  wkd_ind product  \\\n77          231   广州      线下       Female      >=60  Weekend      毛衣   \n837         231   广州      线下       Female     40-44  Weekend      袜子   \n1137        336   西安      线下       Female     45-49  Weekend    当季新品   \n1405        231   广州      线下       Female     35-39  Weekday    当季新品   \n1411        231   广州      线下       Female     30-34  Weekend    当季新品   \n2475        336   西安      线下       Female     45-49  Weekend      T恤   \n2606        231   广州      线下       Female     20-24  Weekday      T恤   \n2863        231   广州      线下         Male     40-44  Weekend    当季新品   \n3479        231   广州      线下         Male      >=60  Weekend     牛仔裤   \n4168        231   广州      线下       Female     25-29  Weekend      袜子   \n4390        231   广州      线下       Female     25-29  Weekend      袜子   \n4858        231   广州      线下       Female     20-24  Weekend      T恤   \n5048        231   广州      线下         Male     30-34  Weekday    当季新品   \n7656        231   广州      线下       Female     50-54  Weekday      T恤   \n9278        336   西安      线下       Female       <20  Weekend      T恤   \n10558       336   西安      线下       Female     30-34  Weekday      T恤   \n11120       336   西安      线下       Female     25-29  Weekend      袜子   \n12703       231   广州      线下       Female     40-44  Weekday      T恤   \n12764       231   广州      线下         Male     50-54  Weekend      T恤   \n12778       336   西安      线下       Female     20-24  Weekend      T恤   \n13323       336   西安      线下       Female     40-44  Weekday     牛仔裤   \n16261       231   广州      线下       Female     35-39  Weekday      T恤   \n17259       336   西安      线下       Female     20-24  Weekday      配件   \n17606       231   广州      线下       Female     20-24  Weekend    当季新品   \n17967       336   西安      线下         Male     50-54  Weekday      短裤   \n19032       231   广州      线下       Female     25-29  Weekday      短裤   \n19486       336   西安      线下         Male      >=60  Weekday      T恤   \n20049        91   武汉      线上       Female     55-59  Weekday      运动   \n20180       336   西安      线下         Male     20-24  Weekday      T恤   \n21404       336   西安      线下         Male      >=60  Weekday      短裤   \n21618       231   广州      线下         Male     40-44  Weekend      袜子   \n\n       customer  revenue  order  quant  unit_cost  unit_price  \n77            1     0.00      1      1         99           0  \n837           1     0.00      1      1          9           0  \n1137          1     0.00      1      1         59           0  \n1405          1     0.00      1      1         59           0  \n1411          1     0.00      1      1         59           0  \n2475          1     0.00      1      1         49           0  \n2606          1     0.00      1      1         49           0  \n2863          1     0.00      1      1         59           0  \n3479          1     0.00      1      1         69           0  \n4168          1     0.00      1      1          9           0  \n4390          1     0.00      1      1          9           0  \n4858          1     0.00      1      1         49           0  \n5048          1     0.00      1      1         59           0  \n7656          1     0.00      1      1         49           0  \n9278          1     0.00      1      1         49           0  \n10558         1     0.00      1      1         49           0  \n11120         1     0.00      1      1          9           0  \n12703         1     0.00      1      1         49           0  \n12764         1     0.00      1      1         49           0  \n12778         1     0.00      1      1         49           0  \n13323         1     0.00      1      1         69           0  \n16261         1     0.00      1      1         49           0  \n17259         1     0.00      1      1         29           0  \n17606         1     0.00      1      1         59           0  \n17967         1     0.00      1      1         19           0  \n19032         1     0.00      1      1         19           0  \n19486         1     0.00      1      1         49           0  \n20049         1    -0.66      1      2         49           0  \n20180         1     0.00      1      1         49           0  \n21404         1     0.00      1      1         19           0  \n21618         1     0.00      1      1          9           0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_id</th>\n      <th>city</th>\n      <th>channel</th>\n      <th>gender_group</th>\n      <th>age_group</th>\n      <th>wkd_ind</th>\n      <th>product</th>\n      <th>customer</th>\n      <th>revenue</th>\n      <th>order</th>\n      <th>quant</th>\n      <th>unit_cost</th>\n      <th>unit_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>77</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>&gt;=60</td>\n      <td>Weekend</td>\n      <td>毛衣</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>99</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>837</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>40-44</td>\n      <td>Weekend</td>\n      <td>袜子</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1137</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>45-49</td>\n      <td>Weekend</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1405</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>35-39</td>\n      <td>Weekday</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1411</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>30-34</td>\n      <td>Weekend</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2475</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>45-49</td>\n      <td>Weekend</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2606</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>20-24</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2863</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>40-44</td>\n      <td>Weekend</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3479</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>&gt;=60</td>\n      <td>Weekend</td>\n      <td>牛仔裤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>69</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4168</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekend</td>\n      <td>袜子</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4390</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekend</td>\n      <td>袜子</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4858</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>20-24</td>\n      <td>Weekend</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5048</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>30-34</td>\n      <td>Weekday</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7656</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>50-54</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9278</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>&lt;20</td>\n      <td>Weekend</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10558</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>30-34</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11120</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekend</td>\n      <td>袜子</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12703</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>40-44</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12764</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>50-54</td>\n      <td>Weekend</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12778</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>20-24</td>\n      <td>Weekend</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13323</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>40-44</td>\n      <td>Weekday</td>\n      <td>牛仔裤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>69</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16261</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>35-39</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17259</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>20-24</td>\n      <td>Weekday</td>\n      <td>配件</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>29</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17606</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>20-24</td>\n      <td>Weekend</td>\n      <td>当季新品</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17967</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>50-54</td>\n      <td>Weekday</td>\n      <td>短裤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>19</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19032</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Female</td>\n      <td>25-29</td>\n      <td>Weekday</td>\n      <td>短裤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>19</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19486</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>&gt;=60</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20049</th>\n      <td>91</td>\n      <td>武汉</td>\n      <td>线上</td>\n      <td>Female</td>\n      <td>55-59</td>\n      <td>Weekday</td>\n      <td>运动</td>\n      <td>1</td>\n      <td>-0.66</td>\n      <td>1</td>\n      <td>2</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20180</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>20-24</td>\n      <td>Weekday</td>\n      <td>T恤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>49</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21404</th>\n      <td>336</td>\n      <td>西安</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>&gt;=60</td>\n      <td>Weekday</td>\n      <td>短裤</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>19</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21618</th>\n      <td>231</td>\n      <td>广州</td>\n      <td>线下</td>\n      <td>Male</td>\n      <td>40-44</td>\n      <td>Weekend</td>\n      <td>袜子</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检测数值型数据的异常值\n",
    "# 1. 检测 产品的成本 的异常值\n",
    "# uniqlo_data[uniqlo_data.unit_cost < 0]\n",
    "\n",
    "\n",
    "# 2. 检测 产品的单价 的异常值\n",
    "# uniqlo_data[uniqlo_data.unit_price < 0]\n",
    "\n",
    "# 3. 检测 revenue 的异常值\n",
    "uniqlo_data[uniqlo_data.revenue <= 0]\n",
    "# uniqlo_data[uniqlo_data.revenue > 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f429cc",
   "metadata": {},
   "source": [
    "#### 从数据可看出，销售金额“revenue”存在'<0'异常值\n",
    "#### 接下来对销售金额进行描述性统计分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0afc29c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T07:07:47.750468300Z",
     "start_time": "2024-08-11T07:07:47.736225900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "count    22293.000000\nmean       159.531371\nstd        276.254066\nmin         -0.660000\n25%         64.000000\n50%         99.000000\n75%        175.000000\nmax      12538.000000\nName: revenue, dtype: float64"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniqlo_data.revenue.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47d07a15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T07:08:42.658940600Z",
     "start_time": "2024-08-11T07:08:42.655374200Z"
    }
   },
   "outputs": [],
   "source": [
    "# uniqlo_data.revenue.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904bf740",
   "metadata": {},
   "source": [
    "从对消除金额进行概括性描述可以看出，max部分过于大可能存在企业或者个人批量订购的情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23598733",
   "metadata": {},
   "source": [
    "## 一、整体销售情况随着时间的变化是怎样的？\n",
    "  为防止销售金额的异常值对数据的分析进行干扰。筛选掉销售金额小于零的这一部分和销售金额大于两千的部分。然后进行分析。\n",
    "\n",
    "数据中与时间有关的字段仅为类别变量wkd_ind代表的Weekday和Weekend，即购买发生的时间是周中还是周末。\n",
    "本题意为分析对比周末和周中与销售有关的数据，包括产品销售数量quant、销售金额revenue、顾客人数customer的情况，可生成柱状图进行可视化。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96f8a838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除异常值，形成新的数据\n",
    "uniqlo_data_1 = uniqlo_data[uniqlo_data.revenue > 0]\n",
    "uniqlo_data_1 = uniqlo_data_1[uniqlo_data_1.revenue < 2000]\n",
    "# uniqlo_data_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4040e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>revenue</th>\n",
       "      <th>store_id</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wkd_ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <td>20378</td>\n",
       "      <td>20676</td>\n",
       "      <td>23213</td>\n",
       "      <td>1927993.60</td>\n",
       "      <td>4119500</td>\n",
       "      <td>571203</td>\n",
       "      <td>1033608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekend</th>\n",
       "      <td>14626</td>\n",
       "      <td>14748</td>\n",
       "      <td>16445</td>\n",
       "      <td>1398312.33</td>\n",
       "      <td>3329647</td>\n",
       "      <td>452697</td>\n",
       "      <td>835695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         customer  order  quant     revenue  store_id  unit_cost  unit_price\n",
       "wkd_ind                                                                     \n",
       "Weekday     20378  20676  23213  1927993.60   4119500     571203     1033608\n",
       "Weekend     14626  14748  16445  1398312.33   3329647     452697      835695"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wkd_ind_sum = uniqlo_data_1.pivot_table(index='wkd_ind', aggfunc='sum')\n",
    "wkd_ind_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8770a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'customer')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('周中和周末 销售金额 对比')\n",
    "uniqlo_data_1.groupby('wkd_ind')\n",
    "\n",
    "# 使用透视表，使用 wkd_ind进行分组求和\n",
    "wkd_ind_sum = uniqlo_data_1.pivot_table(index='wkd_ind', aggfunc='sum')\n",
    "wkd_ind_sum.revenue.plot.bar(rot=0, color=['b', 'orange'])\n",
    "plt.ylabel('revenue')\n",
    "\n",
    "# 计算\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title('周中和周末 订单数量 对比')\n",
    "wkd_ind_count = uniqlo_data_1.pivot_table(index='wkd_ind', aggfunc=len)\n",
    "wkd_ind_count.age_group.plot.bar(rot=0, color=['b', 'orange'])\n",
    "plt.ylabel('count')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title('周中和周末 购买的产品数量 对比')\n",
    "wkd_ind_sum.quant.plot.bar(rot=0, color=['b', 'orange'])\n",
    "plt.ylabel('quant')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title('周中和周末 客户数量 对比')\n",
    "wkd_ind_sum.customer.plot.bar(rot=0, color=['b', 'orange'])\n",
    "plt.ylabel('customer')\n",
    "\n",
    "# pivot_sum\n",
    "# pivot_count\n",
    "\n",
    "# sns.barplot(x='wkd_ind',y='revenue',data=UNIQLO_1)\n",
    "# sns.countplot(x='wkd_ind',data=UNIQLO_1)\n",
    "# sns.barplot(x='wkd_ind',y='quant',data=UNIQLO_1)\n",
    "# sns.barplot(x='wkd_ind',y='customer',data=UNIQLO_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06a7cf6",
   "metadata": {},
   "source": [
    "### 结论：\n",
    "  从销售额和交易次数与时间的关系图可以发现，工作日的总交易次数和平均销售额都略高于周末。工作日每笔订单的商品数量和消费者数量也都略高于周日。\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49114d49",
   "metadata": {},
   "source": [
    "### 二、不同产品的销售情况是怎样的？顾客偏爱哪一种购买方式？\n",
    "1. 不同产品即指product字段中不同类别的产品，销售情况即为销售额revenue，可生成柱状图进行可视化\n",
    "\n",
    "2. 购买方式只有channel是线上还是线下这一个指标，而顾客可以从不同性别gender_group、年龄段age_group、城市city三个维度进行分解，因此本问即为探究不同性别、年龄段和城市的顾客对线上、线下两种购买方式的偏好，可生成柱状图进行可视化的呈现\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d78d8187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '(单位百万元)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 不同产品即指product字段中不同类别的产品，销售情况即为销售额revenue，可生成柱状图进行可视化\n",
    "\n",
    "# 各种产品类别 product 的销售额 进行分析\n",
    "product_sum = uniqlo_data_1.pivot_table(index='product', aggfunc=\"sum\")\n",
    "product_sum\n",
    "product_sum['revenue'].plot.bar(color=list(\"rgb\"), rot=0)\n",
    "plt.title('不同类型的产品的总销售额')\n",
    "plt.ylabel('(单位百万元)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e595ce38",
   "metadata": {},
   "source": [
    "2. 购买方式只有channel是线上还是线下这一个指标，而顾客可以从不同性别gender_group、年龄段age_group、城市city三个维度进行分解，因此本问即为探究不同性别、年龄段和城市的顾客对线上、线下两种购买方式的偏好，可生成柱状图进行可视化的呈现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e5c9508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'线上和线下销售数量对比'}, xlabel='channel'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 线上和线下的销售额对比。\n",
    "channel_sum = uniqlo_data_1.pivot_table(index='channel', aggfunc='sum')\n",
    "plt.figure()\n",
    "plt.title('线上和线下销售金额对比')\n",
    "channel_sum.revenue.plot.bar(color=['b', 'orange'], rot=0)\n",
    "plt.figure()\n",
    "plt.title('线上和线下销售数量对比')\n",
    "\n",
    "uniqlo_data_1.pivot_table(index='channel', aggfunc=len).revenue.plot.bar(color=['b', 'orange'], rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da30d699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">customer</th>\n",
       "      <th colspan=\"2\" halign=\"left\">order</th>\n",
       "      <th colspan=\"2\" halign=\"left\">quant</th>\n",
       "      <th colspan=\"2\" halign=\"left\">revenue</th>\n",
       "      <th colspan=\"2\" halign=\"left\">store_id</th>\n",
       "      <th colspan=\"2\" halign=\"left\">unit_cost</th>\n",
       "      <th colspan=\"2\" halign=\"left\">unit_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>channel</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>4228</td>\n",
       "      <td>19622</td>\n",
       "      <td>4279</td>\n",
       "      <td>19898</td>\n",
       "      <td>4706</td>\n",
       "      <td>22130</td>\n",
       "      <td>416709.39</td>\n",
       "      <td>1880923.03</td>\n",
       "      <td>428047</td>\n",
       "      <td>4275515</td>\n",
       "      <td>109329</td>\n",
       "      <td>549217</td>\n",
       "      <td>207892</td>\n",
       "      <td>995338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2214</td>\n",
       "      <td>8815</td>\n",
       "      <td>2227</td>\n",
       "      <td>8894</td>\n",
       "      <td>2555</td>\n",
       "      <td>10132</td>\n",
       "      <td>201011.84</td>\n",
       "      <td>817558.81</td>\n",
       "      <td>249709</td>\n",
       "      <td>2458184</td>\n",
       "      <td>64021</td>\n",
       "      <td>296391</td>\n",
       "      <td>119905</td>\n",
       "      <td>537206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             customer        order        quant           revenue              \\\n",
       "channel            线上     线下    线上     线下    线上     线下         线上          线下   \n",
       "gender_group                                                                    \n",
       "Female           4228  19622  4279  19898  4706  22130  416709.39  1880923.03   \n",
       "Male             2214   8815  2227   8894  2555  10132  201011.84   817558.81   \n",
       "\n",
       "             store_id          unit_cost         unit_price          \n",
       "channel            线上       线下        线上      线下         线上      线下  \n",
       "gender_group                                                         \n",
       "Female         428047  4275515    109329  549217     207892  995338  \n",
       "Male           249709  2458184     64021  296391     119905  537206  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_table = uniqlo_data_1.pivot_table(\n",
    "    index='gender_group', columns='channel' , aggfunc=sum\n",
    ")\n",
    "# 去掉未知性别的数据。\n",
    "gender_table = gender_table.loc[['Female','Male']]\n",
    "gender_table.revenue.plot.bar()  # 销售金额。\n",
    "gender_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8775c9eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Female'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_table.revenue.loc['Female'].plot.pie(autopct='%.1f%%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed78914a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Male'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_table.revenue.loc['Male'].plot.pie(autopct='%.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b82af688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age_group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">city</th>\n",
       "      <th colspan=\"2\" halign=\"left\">customer</th>\n",
       "      <th colspan=\"2\" halign=\"left\">order</th>\n",
       "      <th colspan=\"2\" halign=\"left\">product</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">revenue</th>\n",
       "      <th colspan=\"2\" halign=\"left\">store_id</th>\n",
       "      <th colspan=\"2\" halign=\"left\">unit_cost</th>\n",
       "      <th colspan=\"2\" halign=\"left\">unit_price</th>\n",
       "      <th colspan=\"2\" halign=\"left\">wkd_ind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>channel</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>...</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "      <th>线上</th>\n",
       "      <th>线下</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>...</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "      <td>2381</td>\n",
       "      <td>11743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>...</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "      <td>1469</td>\n",
       "      <td>6489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             age_group         city        customer        order         \\\n",
       "channel             线上     线下    线上     线下       线上     线下    线上     线下   \n",
       "gender_group                                                              \n",
       "Female            2381  11743  2381  11743     2381  11743  2381  11743   \n",
       "Male              1469   6489  1469   6489     1469   6489  1469   6489   \n",
       "\n",
       "             product         ... revenue        store_id        unit_cost  \\\n",
       "channel           线上     线下  ...      线上     线下       线上     线下        线上   \n",
       "gender_group                 ...                                            \n",
       "Female          2381  11743  ...    2381  11743     2381  11743      2381   \n",
       "Male            1469   6489  ...    1469   6489     1469   6489      1469   \n",
       "\n",
       "                    unit_price        wkd_ind         \n",
       "channel          线下         线上     线下      线上     线下  \n",
       "gender_group                                          \n",
       "Female        11743       2381  11743    2381  11743  \n",
       "Male           6489       1469   6489    1469   6489  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_table = uniqlo_data_1.pivot_table(\n",
    "    index='gender_group', columns='channel' , aggfunc=len\n",
    ")\n",
    "# 去掉未知性别的数据。\n",
    "gender_table = gender_table.loc[['Female','Male']]\n",
    "gender_table.revenue.plot.bar()  # 销售金额。\n",
    "gender_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01d0f773",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7bab8194",
   "metadata": {},
   "source": [
    "### 结论\n",
    "1. 各个商品种类的平均销售额和交易次数分布图，可以看出T袖占了主要份额，而毛衣、配件和裙子的平均销售额较高。\n",
    "2. 线下交易数量远远大于线上，女性和男性都普遍喜欢线下交易，但线上交易的销售额都略高于线下。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9043c6",
   "metadata": {},
   "source": [
    "检查城市列数据："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52045fa9",
   "metadata": {},
   "source": [
    "### 根据各个城市进行分组,查看线上和线下的销售金额的数据。给出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "19c179e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='city'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "city_table = uniqlo_data_1.pivot_table(\n",
    "    index='city', columns='channel' , aggfunc=len\n",
    ")\n",
    "# 去掉未知性别的数据。  <------ 此处注释写错了。\n",
    "city_table.revenue.plot.bar()  # 销售金额。\n",
    "# city_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4ae72c",
   "metadata": {},
   "source": [
    "### 结论\n",
    "  没有深圳、杭州、北京、南京和成都的线上数据，首先需要确认数据来源是否可靠，找出缺失原因和相应的对策。从已有的数据可以看出，深圳和杭州的交易数量遥遥领先，重庆、西安和上海的线下交易次数远高于线上，而广州则相反。而从交易额的角度看，线上交易额都略高于线下。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc32956",
   "metadata": {},
   "source": [
    "### 根据购买人群的年龄分布进行分组。查看不同年龄的人群对于线上和线下购买的情况。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e1e798a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age_group'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_group_table = uniqlo_data_1.pivot_table(\n",
    "    index='age_group', columns='channel' , aggfunc=len\n",
    ")\n",
    "age_group_table = age_group_table.iloc[:-1]  # 去掉最后一个\n",
    "# 去掉未知性别的数据。\n",
    "age_group_table.revenue.plot.bar()  # 销售金额。\n",
    "# city_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc727d7",
   "metadata": {},
   "source": [
    "### 结论\n",
    "从年龄的角度看，优衣库的线上和线下交易量都主要集中在20-40岁之间，这部分顾客的线上交易额都略高于线下；而40岁以上和20岁以下的顾客，则消费能力低。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80374ed3",
   "metadata": {},
   "source": [
    "### 三、 销售额和产品成本之间的关系怎么样？\n",
    "每单顾客的总销售额为revenue，根据数量quant可以计算出单件产品销售金额，又已知单件产品成本为unit_cost和其类别product。\n",
    "\n",
    "思路一：单件产品销售额-成本为利润margin，margin是如何分布的？是否存在亏本销售的产品？\n",
    "\n",
    "思路二：探究实际销售额和产品成本之间的关系，即为求它们之间的相关，若成正相关，则产品成本越高，销售额越高，或许为高端商品；若成负相关，则成本越低，销售额越高，为薄利多销的模式。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ffb1eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22293.000000\n",
       "mean        38.141038\n",
       "std         40.268799\n",
       "min        -99.000000\n",
       "25%         14.000000\n",
       "50%         30.000000\n",
       "75%         50.000000\n",
       "max        270.000000\n",
       "Name: margin, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算每一单的具体成交价格。\n",
    "uniqlo_data_2 = uniqlo_data.copy()  # 复制一份副本数据。\n",
    "# 计算本单商品的销售单价。\n",
    "uniqlo_data_2['price'] = uniqlo_data_2['revenue'] / uniqlo_data_2['quant']\n",
    "# 计算每一种商品的销售利润。\n",
    "uniqlo_data_2['margin'] = uniqlo_data_2['price'] - uniqlo_data_2['unit_cost']\n",
    "# 对利润执行描述统计。\n",
    "uniqlo_data_2['margin'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5deaffab",
   "metadata": {},
   "source": [
    "根据对利润进行描述统计分析可以看出。一半商品利润在十四元到五十元之间不等。\n",
    "\n",
    "最大利润有二百七十元。还有部分商品可能会亏损。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11df8f4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lEbFd/QBvA1YAT5TfpwNHA5cBhwHLyPBcVn6ub2MZLwG+Ra54ryQDegJ50PXNgMgP7NDrTwZeUx4L2JVceQ4B7ip1riw/LwNuGGW51wC7NzyfCVzWRv3vB64HPlv+F+8GjgX2KeMPBe4mV6ovkSvYdxue3wXsv5Xv0+fJCw2eBqwowyYAk8rjicB9ZfzkMmxnYAA4sTzvLbW+DZhOXhtwE3Bb+X0z8Lo26zuQvMZggNxo7dPkNV1b/ig1TS7rTD/w+8Bnyrrz44Z1aH6T6Y4s7+dxXfi8TSp/+7OAE8hAmgD8Yxl/yyjTda2mhmXsBFy0mfHTgC+TObACOLcM/yjZGOtGPc8p79uN5fHEhvFDn4NDgavK6+8sw5Z1spbtto9b0oMR8WxJJ5MHQfcD7ieDcyLwCbKV8NqIOLaN+Z8JfAPYC3g+8CkynC8uLfKPki3j/YC9gZ8AvyJDcBJwVkR8X9LbSl1DQfFPwOXA68uiIkqLTdKngX8jz4MHeAawX0S8aQtrV5Q3TtI9ZGv72cDeEfEvI147m2xNnh0RG4b2EiJbBW0ptzi4ndyzmAA8D/haeXxHRJwv6fXAmRHR3zDdPcDFEXFTQ20vIzeMZwEXkhvSTwJ/Cvwv+f9rbOW1WuM5pb4zgdPIDchy4L/KS/YgW5FdWf4oNT2dXC/6gB8AF0TEryR9MSKOK619NS5P0huAE4HTI+KxTtQxoqZZwAfJRswD5Dr8++QVz/8BHAx8LCLOG6+amtR4PBmCje/DJODGiNjQ8Lr9gPeRn+cHgB2AT0TE5zpUxwnAa8m9Zsh16qSI+EEZvyIi+iXdTm7U/pPcq/sRedr018n394itraWGrpJnkB+2dWQoHj9ifDsf6mvJFsavyRV2GuW8dEkzI+KPgaNLd8kbyRD/CbkBGSBXhl9IOgs4g9yF+x3ywziN/GBeRZ4m+T5yIwPwATIwhjxC6UbZQqdKeh4Z1gcCXwAeAr7a5LUXAh8HLgLeDryX7N74aBvLHXIpcGpE3C3paeTtD/qHRpZ+5T8DfiJpYsNGYlpE3FQCakJEfL1M/0LytgjPJHeLnwNcQa6f55Ib2C11NcPv7e7Az4BVEXFGqfGWLi9/ExGxXtJVwGuAzwHnSDoaOFjSCnI9OlYSDeHdC1wzFJClXiLiZx2q6buSXknuvX2EXCefCZwDnEee2na+pAnjVVOZ52xyHf0++Z7sDCwmP0+/JoP7HZLmke/Tk8D6iHi1pE+Q6/3LyS6MTvkV8E3gv8nu0mnALElXl/GzJf0Z8FOygfkW4EUR8XiT7rCtUkNw3w88ndz9WM2mwb3FIvvsNiFpF+AmSb9H9vOtIVvih5Ar6+fIELhd0sXAUrIP9cQyfAnZgn8hueJ/NCI+IekZZNfCkMvJrfYrynIBXhURgy3+CWvI/8t3gOUR8XJJewIHSjpxqIUh6Vxgz8h7yrxF0geAX0RE26FdWux/RK68zcb3kK2Sy8mW7AfLRiaAmSWgBPytpAfJjdrjwHvIDd995P/1aLLVu2HkMlo09D7sDRxFbtSOlrSsjN9f0kFdXP4mJE0kNwh9ZJfcSeQe3I1kSN1MtsgXShoKnJnAE6WRMORjZFdOJ2ragfx7X0Q2LC4m90TWko2Zy8a7JoCI+Dowv+yNXKs8YHxEqfVNEXFpqX9f4EPkOjfUL787eWxpJ+DBTtVE6ccmu9JOIBthU4C7ohx/k7Q3cC/wUuCUiHi8cQal0dKzNXu8UEFwR8S/Surodf+SzgOOA0YedJpIblUfIFvaU4H9yd2eC8ldr/dExNXlDTicXNF3JVsApwE3AI+NmO8E4MGIOENSP9m1MBM4LSLWNGyxW6n9d4F9yC6c/cmDoveSregHga8oj2hfTAbkujLp+WQXwHNbXdYojicP+j0p6cnyt80ugSiyj/TvImKppJMj4uyG2r82omV+cJnXIvKDtg/5nuxd6nwzuTF8dEuLjIivSvo22Ur7hzKPd0XEJ8su9XGl1q4sfxR/CPwzG+/tvRP4CrkxvCkirmT4gCqlBbcmIm7oUA0jnUp23+1CtrbXkCE+SB5TCuClpa7xqqmZc4G/ID+fL5X0rYhYRh4Du5z8vD5BHkeaGhFPlD2/n3awhiA/Z58lN1IfZ8Qef+k+3Zc8nnGVpJ+X6Z4v6UvkOndDmbZt231wd0NEvI/c5drIUIubbOFfR+5eQ25V9yT7+24srZQ/j4i7JD1EHujcidziP0r2E260yCbPmw1rxR7khmIV+QHfPyIaT0WcSIbO1yLiSknLSt/qk+Tew8clvaXdLX5EfJ6GvQdJU4Db2tkNjIhvlv/l/WR4vaL8bb8sf+fh5HtwaTu1kntCK8m+7ROAt0u6mezX3nEclr+RiFgKUDZox5U9oE+W2m4FlkhatLWtsS2s6cpS09HkQe5PkevvKaVRcUun+vjbVfYmTyL3otaTn7fFkr4HnEIeoD8AOF7SfWRfMuRnsmN7TGTDpJfsAoHc0Kuhzh3JDQvknm1/w7hlEfGyThVSS3AP/XOa1iupJyKebDZulNevILeUI1fIiQAR8RNy93ro9XsBfxURp4+Yz2nkaYIPkX3xzyD7L9/ZpP5jJN1F9tVdU4ZdX7bIs8iDGGOKiLvJPYChGkaee3sVeabKUAtpX+BfgfMi4qKyt7FK0psi4r5WljmGnlZeVLpQ1GxU+f1q4P9FxB3K82E/FFt3muRk4B3kLRnmk4F8BbkBfjUZBF1b/tjl6Uxgj4h4h6RXRsTlytslH092nQyZRGf7aUczmdxInQO8CfiMpJ8BBzV57bjUVA6CziU/i3s39qGXDe6+ZOv1EeAO4E6yn/7M8rI92LQRtTUmkJ/VofO1n1mGDX0GXkyebfOFDi6zqe02uCOi8VaxQ+H0t2T3BeQuMOQKdArZcml13v3NhpcW960jhu1Fnnr3mSaT3ECG9fVkf+hDwJzI8zb3IrtWIN/Y20pXyXxyV7SHPCK9prR22t2l+7GkL5O7+DuQG6P7Su3TyINuZ0fE9yD3NpRHvR9oc3kj9TD8/vxGaYn3lMcTyVMQm/0PJ5XXBXlKF+R7vLUXUbwOuDbydgzXS1rCcGgfAFwr6U5yo9aN5W/OVHLd+XTDcyLi7Y0vkvQh8oD4K7pYy5Ap5QyNb5SN3sll3byt8cDkeNUk6TDyFNeDyG6kO0pdE8j35ulkX/d7GqZ5LbkX+l+S7gC+FxFrOljWJOD9EXF1Wd7V5B72WcprTfYlz06axKafibYuJhrNdns64PZEGj79zurh9611kqZExBPbuo6toY3PfBnP5e5ABvUvI+LnZe+yJyJ+Mcak7S/T67WZWV2qvFeJmdlvMwe3mVllHNz2W03SGZLO2MJpZpbz8c22CQe32ZabSd4oymyb8MFJq5akC8jL2nckr/Q7mbxT3OeBN0TE88rrPkLeeOgx8vLtDeQFVruQF9sMnZZHuSq2nwzm95KXoj+/vO5kYCHwhjLtGvKUzlZvVWDWEW5xW+1Wlrut/Q95bvGzyDv6DYX2ceQ9keeT506/i7yi8uGIeAl5AdVojidv23k48DfAoeUeGX8CXB0R/Q5t2xa22wtwzFq0uvz+BtmFsQ74cMP4g8ib/lB+n0i2vIfu+DfQZJ5Tyu/nkPfxICK+qHH+Bhiz0XhFtNodVn4fQt5k6/9GXITxLfL2upTf3yJb2Qc1TAd5S9WdyuNjyu8HyG94QdKp5E2OIG9mtGMZ3uwyfrOucnBb7V5Q7j2zC03uERERt5C3IF1JXu7+fvL2vPuX6fYvL10OnCTpMobvPfEFIMql8aeTtw+FvKXAAWWeCzv/J5ltng9OWrXKwckVEbFiG5diNq4c3GZmlXFXiZlZZRzcZmaVcXCbmVXGwW1mVhkHt5lZZRzcZmaV+f+xhNo1hGCQ6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 给不同商品的利润进行分析。\n",
    "product_table = uniqlo_data_2.pivot_table(index='product')\n",
    "product_table['margin'].plot.bar(rot=0, color=list(\"rgb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e83391c",
   "metadata": {},
   "source": [
    "毛衣、配件和裙子的平均利润远高于其他商品，运动、短裤和牛仔裤是相对利润较低的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "22306fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>margin_range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-100&lt;=x&lt;-50</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>毛衣</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-50&lt;=x&lt;0</th>\n",
       "      <td>2795</td>\n",
       "      <td>9</td>\n",
       "      <td>牛仔裤</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0&lt;=x&lt;50</th>\n",
       "      <td>11028</td>\n",
       "      <td>9</td>\n",
       "      <td>T恤</td>\n",
       "      <td>5755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50&lt;=x&lt;100</th>\n",
       "      <td>6286</td>\n",
       "      <td>9</td>\n",
       "      <td>T恤</td>\n",
       "      <td>3843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101&lt;=x&lt;150</th>\n",
       "      <td>1684</td>\n",
       "      <td>7</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151&lt;=x&lt;200</th>\n",
       "      <td>401</td>\n",
       "      <td>4</td>\n",
       "      <td>配件</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201&lt;=x&lt;250</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>T恤</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251&lt;=x&lt;300</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>配件</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count unique   top  freq\n",
       "margin_range                          \n",
       "-100<=x<-50      22      3    毛衣     9\n",
       "-50<=x<0       2795      9   牛仔裤   820\n",
       "0<=x<50       11028      9    T恤  5755\n",
       "50<=x<100      6286      9    T恤  3843\n",
       "101<=x<150     1684      7  当季新品   352\n",
       "151<=x<200      401      4    配件   150\n",
       "201<=x<250       46      3    T恤    27\n",
       "251<=x<300       31      1    配件    31"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[-100,-50,0,50,100,150,200, 250, 300]\n",
    "labels=['-100<=x<-50','-50<=x<0','0<=x<50','50<=x<100',\n",
    "        '101<=x<150','151<=x<200', '201<=x<250', '251<=x<300']\n",
    "\n",
    "uniqlo_data_2['margin_range']=pd.cut(uniqlo_data_2.margin,bins,right=False,labels=labels)\n",
    "# uniqlo_data_2\n",
    "uniqlo_data_2[uniqlo_data_2.margin < 0].pivot_table(\n",
    "    index='margin_range', columns='product', aggfunc=len)['wkd_ind'].plot.bar()\n",
    "# range_table = uniqlo_data_2.pivot_table(index='margin_range', columns='product', aggfunc=len)\n",
    "# range_table['unit_cost'].plot.bar()\n",
    "uniqlo_data_2.groupby(['margin_range'])['product'].describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d057de50",
   "metadata": {},
   "source": [
    "### 结论:\n",
    "根据利润对数据进行分组，可以看出，亏损商品主要来源于牛仔裤和毛衣，而T恤的销量最高，单件利润集中在0-100元之间，应该是优衣库利润的中坚力量。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31ec33e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>channel</th>\n",
       "      <th>city</th>\n",
       "      <th>customer</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>margin</th>\n",
       "      <th>order</th>\n",
       "      <th>price</th>\n",
       "      <th>product</th>\n",
       "      <th>quant</th>\n",
       "      <th>revenue</th>\n",
       "      <th>store_id</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>wkd_ind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>margin_range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-100&lt;=x&lt;-50</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-50&lt;=x&lt;0</th>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "      <td>2795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0&lt;=x&lt;50</th>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "      <td>11028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50&lt;=x&lt;100</th>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "      <td>6286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101&lt;=x&lt;150</th>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "      <td>1684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151&lt;=x&lt;200</th>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201&lt;=x&lt;250</th>\n",
       "      <td>46</td>\n",
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       "      <td>46</td>\n",
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       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>251&lt;=x&lt;300</th>\n",
       "      <td>31</td>\n",
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       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age_group  channel   city  customer  gender_group  margin  \\\n",
       "margin_range                                                              \n",
       "-100<=x<-50          22       22     22        22            22      22   \n",
       "-50<=x<0           2795     2795   2795      2795          2795    2795   \n",
       "0<=x<50           11028    11028  11028     11028         11028   11028   \n",
       "50<=x<100          6286     6286   6286      6286          6286    6286   \n",
       "101<=x<150         1684     1684   1684      1684          1684    1684   \n",
       "151<=x<200          401      401    401       401           401     401   \n",
       "201<=x<250           46       46     46        46            46      46   \n",
       "251<=x<300           31       31     31        31            31      31   \n",
       "\n",
       "              order  price  product  quant  revenue  store_id  unit_cost  \\\n",
       "margin_range                                                               \n",
       "-100<=x<-50      22     22       22     22       22        22         22   \n",
       "-50<=x<0       2795   2795     2795   2795     2795      2795       2795   \n",
       "0<=x<50       11028  11028    11028  11028    11028     11028      11028   \n",
       "50<=x<100      6286   6286     6286   6286     6286      6286       6286   \n",
       "101<=x<150     1684   1684     1684   1684     1684      1684       1684   \n",
       "151<=x<200      401    401      401    401      401       401        401   \n",
       "201<=x<250       46     46       46     46       46        46         46   \n",
       "251<=x<300       31     31       31     31       31        31         31   \n",
       "\n",
       "              unit_price  wkd_ind  \n",
       "margin_range                       \n",
       "-100<=x<-50           22       22  \n",
       "-50<=x<0            2795     2795  \n",
       "0<=x<50            11028    11028  \n",
       "50<=x<100           6286     6286  \n",
       "101<=x<150          1684     1684  \n",
       "151<=x<200           401      401  \n",
       "201<=x<250            46       46  \n",
       "251<=x<300            31       31  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "margin_range_table = uniqlo_data_2.pivot_table(index='margin_range', aggfunc=len)\n",
    "margin_range_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9561e6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3116\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "margin_range_table = uniqlo_data_2.pivot_table(index='margin_range', aggfunc=len)\n",
    "margin_range_table['city'].plot.bar()\n",
    "margin_range_table\n",
    "print(len(uniqlo_data_2[uniqlo_data_2.margin<=0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098b9120",
   "metadata": {},
   "source": [
    "### 分析各个字段的相关性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "603ec0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>margin</th>\n",
       "      <th>revenue</th>\n",
       "      <th>unit_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>margin</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.33337</td>\n",
       "      <td>0.10275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>0.33337</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.14844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_cost</th>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.14844</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            margin  revenue  unit_cost\n",
       "margin     1.00000  0.33337    0.10275\n",
       "revenue    0.33337  1.00000    0.14844\n",
       "unit_cost  0.10275  0.14844    1.00000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfields_table = uniqlo_data_2[['margin', 'revenue', 'unit_cost']]\n",
    "myfields_table.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72a614d",
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   "source": [
    "利润和销售额成正相关，且相关性一般，虽然利润与成本也呈正相关，但相关性较弱。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df48d188",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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